R Bootcamp Einstein 2017
SECTION 1 - The Basics
Installing R
If you don’t have them already installed you need to download and install R and R studio
- Download R
- R Studio
Introduction to R
- R is an open source programming environment
- It is most commonly used for statistical computing and graphics
- The basic functionality of R can be extended with ‘packages’
Resources for R
- R for data science http://r4ds.had.co.nz
- Served as inspiration for a lot of this bootcamp
- Written by programmers at R Studio
- R Programming for Data Science https://bookdown.org/rdpeng/rprogdatascience/
- R Bootcamp https://www.jaredknowles.com/r-bootcamp/
Introduction to R studio
- R studio is an IDE within which you can write/edit code, run code, display plots/graphics and a whole host of other functions
Getting Started
- Go to https://github.com/mseinstein/ to download the Presentions > RBootCamp > r_bootcamp_einstein_2017.R file
- All the code presented here is within that R file
Running code in R
- To run any line of code use CTRL+Enter or Cmd+Enter
- R will run the highlighted code OR
- R will run the line of code which the cursor is on
Installing and Loading packages
- The power of R can be extended with packages
- Tidyverse is a collection of R packages that share common philosophies and are designed to work together
- This is the main package we will be using
install.packages("tidyverse") # only needs to be done once per machine
library(tidyverse) # needs to be loaded every session you want to use it (usually everytime you open R studio)
Key Symbols
- < -
- Assignment operator, e.g. x <- 5
- The equals sign, =, will work as a substitution in most but not all cases
#- Denotes a comment
- All text following the
#will be ignored
?function_name- Opens the Documentation for the function
??term- Searches the help documentation for the queried string
SECTION 2 - Data Visualisations with ggplot2
- ggplot2 is a plotting system for R, based on the grammar of graphics
- See http://vita.had.co.nz/papers/layered-grammar.pdf for an in-depth discussion
library(tidyverse)
Graphics in ggplot are built in layers
mpg # dataset built in to ggplot## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29
## 3 audi a4 2.0 2008 4 manual(m6) f 20 31
## 4 audi a4 2.0 2008 4 auto(av) f 21 30
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26
## 7 audi a4 3.1 2008 6 auto(av) f 18 27
## 8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26
## 9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25
## 10 audi a4 quattro 2.0 2008 4 manual(m6) 4 20 28
## # ... with 224 more rows, and 2 more variables: fl <chr>, class <chr>summary(mpg) ## manufacturer model displ year
## Length:234 Length:234 Min. :1.600 Min. :1999
## Class :character Class :character 1st Qu.:2.400 1st Qu.:1999
## Mode :character Mode :character Median :3.300 Median :2004
## Mean :3.472 Mean :2004
## 3rd Qu.:4.600 3rd Qu.:2008
## Max. :7.000 Max. :2008
## cyl trans drv cty
## Min. :4.000 Length:234 Length:234 Min. : 9.00
## 1st Qu.:4.000 Class :character Class :character 1st Qu.:14.00
## Median :6.000 Mode :character Mode :character Median :17.00
## Mean :5.889 Mean :16.86
## 3rd Qu.:8.000 3rd Qu.:19.00
## Max. :8.000 Max. :35.00
## hwy fl class
## Min. :12.00 Length:234 Length:234
## 1st Qu.:18.00 Class :character Class :character
## Median :24.00 Mode :character Mode :character
## Mean :23.44
## 3rd Qu.:27.00
## Max. :44.00mpg$drv## [1] "f" "f" "f" "f" "f" "f" "f" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4"
## [18] "4" "r" "r" "r" "r" "r" "r" "r" "r" "r" "r" "4" "4" "4" "4" "f" "f"
## [35] "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "4" "4" "4"
## [52] "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4"
## [69] "4" "4" "4" "4" "4" "4" "r" "r" "r" "4" "4" "4" "4" "4" "4" "4" "4"
## [86] "4" "4" "4" "4" "4" "r" "r" "r" "r" "r" "r" "r" "r" "r" "f" "f" "f"
## [103] "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"
## [120] "f" "f" "f" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "r" "r"
## [137] "r" "4" "4" "4" "4" "f" "f" "f" "f" "f" "f" "f" "f" "f" "4" "4" "4"
## [154] "4" "f" "f" "f" "f" "f" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4" "4"
## [171] "4" "4" "4" "4" "4" "4" "4" "4" "4" "f" "f" "f" "f" "f" "f" "f" "f"
## [188] "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "4" "4" "4" "4" "4" "4"
## [205] "4" "4" "4" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"
## [222] "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"- The ggplot command begins a plot to which you can add layers
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy)) + geom_point(mapping = aes(x = displ, y = hwy))
By inserting x and y into ggplot all layers will use those parameters unless otherwise specified
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_smooth() + geom_point()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_smooth() + geom_point() +
geom_smooth(mapping = aes(x = displ,y = cty),color = "red")
Note: if you want to plot a variable to a feature like color or size, it must go in the aes() term, if you just want to set them at a certain value they go outside the aes
ggplot(mpg, aes(displ,hwy)) + geom_smooth(color = "green") + geom_point(aes(color=class),size=2) 
You can also split the plot into subplots based on a varible using facet
ggplot(mpg, aes(displ,hwy)) + geom_point() + facet_grid(.~cyl)
ggplot(mpg, aes(displ,hwy)) + geom_point() + facet_grid(cyl~.)
ggplot(mpg, aes(displ,hwy)) + geom_point() + facet_wrap(~cyl)
ggplot(mpg, aes(displ,hwy)) + geom_point() + facet_grid(cyl~class)
Play with ggplot trying to make some of the following plots: 
Graphics and Statistical Transformations
This time we will be using the diamonds dataset
summary(diamonds)## carat cut color clarity
## Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
## 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
## Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
## Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
## 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
## Max. :5.0100 I: 5422 VVS1 : 3655
## J: 2808 (Other): 2531
## depth table price x
## Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
## 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
## Median :61.80 Median :57.00 Median : 2401 Median : 5.700
## Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
## 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
## Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
##
## y z
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.720 1st Qu.: 2.910
## Median : 5.710 Median : 3.530
## Mean : 5.735 Mean : 3.539
## 3rd Qu.: 6.540 3rd Qu.: 4.040
## Max. :58.900 Max. :31.800
## ggplot(diamonds,aes(cut)) + geom_bar()
ggplot(diamonds,aes(price)) + geom_histogram(bins=100)
Bar charts, histograms and the other plots in the one variable section of the ggplot2 cheat sheet bin your data based on a single variable
You can determine the computed variables of a graphic by using the help function:
- Computed variables
- count
- number of points in bin
- prop
- groupwise proportion
- count
ggplot(diamonds) + geom_bar(aes(x=cut,y=..prop..,group=1))
Position adjustments
ggplot(diamonds) + geom_bar(aes(x = color, fill = cut), position = "dodge")
ggplot(diamonds) + geom_bar(aes(x = color, fill = cut), position = "fill")
ggplot(diamonds) + geom_bar(aes(x = color, color = cut), position = "stack",fill=NA)
Some more example plots from http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +
geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +
labs(subtitle="Area Vs Population", y="Population", x="Area", title="Scatterplot", caption = "Source: midwest")
mtcars$`car name` <- rownames(mtcars) # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2) # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above") # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ] # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) # convert to factor to retain sorted order in plot.
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5) +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'", title= "Diverging Bars") +
coord_flip()
# prep data
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$`1952`),sep=", ")
right_label <- paste(df$continent, round(df$`1957`),sep=", ")
df$class <- ifelse((df$`1957` - df$`1952`) < 0, "red", "green")
ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) +
geom_vline(xintercept=1, linetype="dashed", size=.1) +
geom_vline(xintercept=2, linetype="dashed", size=.1) +
scale_color_manual(labels = c("Up", "Down"),
values = c("green"="#00ba38", "red"="#f8766d")) + # color of lines
labs(x="", y="Mean GdpPerCap") + # Axis labels
xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`)))) + # X and Y axis limits
geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5) +
geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5) +
geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5) + # title
geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5) + # title
theme(panel.background = element_blank(),panel.grid = element_blank(),axis.ticks = element_blank(),
axis.text.x = element_blank(),panel.border = element_blank(),plot.margin = unit(c(1,2,1,2), "cm"))
ggplot(mpg, aes(cty)) +
geom_density(aes(fill=factor(cyl)), alpha=0.8) +
labs(title="Density plot",
subtitle="City Mileage Grouped by Number of cylinders",
caption="Source: mpg",
x="City Mileage",
fill="# Cylinders")
ggplot(mpg, aes(class, cty)) +
geom_violin() +
labs(title="Violin plot",
subtitle="City Mileage vs Class of vehicle",
caption="Source: mpg",
x="Class of Vehicle",
y="City Mileage")
theme_set(theme_classic())
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
ggplot(df, aes(x = "", y=freq, fill = factor(class))) +
geom_bar(width = 1, stat = "identity") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust=0.5)) +
labs(fill="class",
x=NULL,
y=NULL,
title="Pie Chart of class",
caption="Source: mpg") +
coord_polar(theta = "y", start=0)
EncSz <- 25
SynPermCon <- 0.5
PtPrcnt <- 0.75
SPSmpSz <- round(EncSz^2*PtPrcnt)
ENC <- rep(.3,EncSz^2)
ENC[c(19:83,200:250,353:420,497:585)] <- 1
SPEncBoxes <- tibble(x = rep(c(1:EncSz),EncSz), y = sort(rep(c(1:EncSz),EncSz)))
j <- rep(NA,EncSz^2)
j[sample(EncSz^2,SPSmpSz)] <- rnorm(SPSmpSz,mean=.9*SynPermCon,sd=SynPermCon/5)
j2 <- rep(NA,EncSz^2)
j2[j>0.5] <- 1
j2[j>0.5 & ENC ==1] <- 2
j2[is.na(j)] <- NA
EncAct <- rep(0.1,EncSz^2)
EncAct[j>SynPermCon] <- 1
j <- cut(j,breaks = c(-Inf,seq(0.4,0.6,0.025),Inf))
BlnkGrph = theme(axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), axis.title.x=element_blank(),
axis.title.y=element_blank(), legend.position="none", panel.background=element_blank(), panel.border=element_blank(), panel.grid.major=element_blank(),
panel.grid.minor=element_blank(), plot.background=element_blank(),plot.margin=grid::unit(c(0,0,0,0), "mm"))
SPEncBoxes %>% ggplot(aes(x,y,fill = factor(round(ENC)))) +
geom_tile(color = "gray",show.legend=FALSE) + BlnkGrph + coord_fixed() +
geom_point(aes(x,y, color = factor(j2)),shape = 16,na.rm=TRUE, size = 3) +
scale_fill_manual(values = c("white","blue")) + scale_shape_identity() +
scale_color_manual(values = c("black","green"))
SPEncBoxes %>% ggplot(aes(x,y,fill = j, color = EncAct)) +
geom_tile(show.legend=FALSE, size = 0.2,alpha=EncAct) + BlnkGrph + coord_fixed() +
scale_color_gradient(low="gray",high ="black") +
scale_fill_manual(values = c("red","red","red","red","orangered", "orange","yellow","lightgreen","green1","green1"),na.value="white")
Spend some time playing with ggplot
- See http://ggplot2.tidyverse.org/reference/ for a full list of functions in ggplot
- Here are a list of the datasets built into ggplot (use ?“dataset name” to find out more about the individual datasets)
- diamonds
- Prices of 50,000 round cut diamonds
- economics
- US economic time series
- faithfuld
- 2d density estimate of Old Faithful data
- midwest
- Midwest demographics
- mpg
- Fuel economy data from 1999 and 2008 for 38 popular models of car
- msleep
- An updated and expanded version of the mammals sleep dataset
- presidential
- Terms of 11 presidents from Eisenhower to Obama
- seals
- Vector field of seal movements
- txhousing
- Housing sales in TX
- luv_colours
- colors() in Luv space
- diamonds
SECTION 2 - Data Manipulation
Basics
Mathematical and Boolean Operators
-sqrt(25) + (5+3)/4*7 - 2^2## [1] 55%/%3 # Integer Division## [1] 15%%3 # Modulo (remainder after division)## [1] 25 == 6## [1] FALSE5 != 6## [1] TRUE83 > (25 >= 23)## [1] TRUE5 > 3 & 3 < 2## [1] FALSE5 > 3 | 3 < 2## [1] TRUEVectors and Sequences
1:4## [1] 1 2 3 4c(5,3,2,1) # Creates a vector via concaternation (hence the c)## [1] 5 3 2 1c(12,1:4,6)## [1] 12 1 2 3 4 6seq(from = 1, t= 10, by = 2) # Creates a vector with the given paramters## [1] 1 3 5 7 9seq(1,10,2) # creates the same vector without naming the paramters## [1] 1 3 5 7 9seq(1,10) # R uses the default values for any empty parameters## [1] 1 2 3 4 5 6 7 8 9 10seq(to = 10, by = 2)## [1] 1 3 5 7 9seq(by = 2, to = 10)## [1] 1 3 5 7 9c(seq(1,10,2), 25, 10)## [1] 1 3 5 7 9 25 10c(seq(1,10,2), 25, 10) > 12## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSEc(seq(1,10,2), 25, 10) * 2## [1] 2 6 10 14 18 50 20Variables and Assignment
x = 5+3
(x = 5+3)## [1] 8x <- 5+3
(x <- 5+3)## [1] 8y <- x
y## [1] 8x <- 5+3 > 2
x## [1] TRUEx <- seq(172,23,-13)
x## [1] 172 159 146 133 120 107 94 81 68 55 42 29Indexing
R starts at 1
x <- seq(172,23,-13)
x[1] ## [1] 172x[c(1,3)] ## [1] 172 146x[2:4] ## [1] 159 146 133x[4:2] ## [1] 133 146 159x[]## [1] 172 159 146 133 120 107 94 81 68 55 42 29x[-1]## [1] 159 146 133 120 107 94 81 68 55 42 29x[-c(1,3)]## [1] 159 133 120 107 94 81 68 55 42 29x[x%%2==0]## [1] 172 146 120 94 68 42y <- x[x%%2==0]
y[9] <- 10
y## [1] 172 146 120 94 68 42 NA NA 10Built in Functions
x <- 1:20
mean(x)## [1] 10.5max(x)## [1] 20min(x)## [1] 1length(x)## [1] 20range(x)## [1] 1 20prod(x)## [1] 2.432902e+18var(x)## [1] 35log(x)## [1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379 1.7917595 1.9459101
## [8] 2.0794415 2.1972246 2.3025851 2.3978953 2.4849066 2.5649494 2.6390573
## [15] 2.7080502 2.7725887 2.8332133 2.8903718 2.9444390 2.9957323sqrt(x)## [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751
## [8] 2.828427 3.000000 3.162278 3.316625 3.464102 3.605551 3.741657
## [15] 3.872983 4.000000 4.123106 4.242641 4.358899 4.472136Exercises
Create a vector of 2 through 8 squared:
4, 9, 16, 25, 36, 49, 64Create a vector of the square roots of the sum of sqaures of every pair of digits of 1 to 100:
sqrt(1^2 + 2^2), sqrt(3^2 + 4^2), sqrt(5^2 + 6^2), … , sqrt(99^2 + 100^2)Create a vector of the numbers 1 to 100 not divisible by 3 or 5:
1, 2, 4, 7, 8, 11, 13, 14, 16, 17, … , 97, 98
Trouble with Tibbles
- Tibbles are a type of data frame (way of representing data) that are lazy (they don’t change variable names or types) and surly (e.g., they complain when a variable does not exist)
- Tibbles differ from traidtional data frames in two key ways, printing and subsetting
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaas_tibble(iris) # shows only a few rows as well as the type of data in each row## # A tibble: 150 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fctr>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ... with 140 more rowsPartial Matching
iris$Spec## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginicaas_tibble(iris)$Spec## Warning: Unknown or uninitialised column: 'Spec'.## NULLSubsetting
iris[1]## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4
## 7 4.6
## 8 5.0
## 9 4.4
## 10 4.9
## 11 5.4
## 12 4.8
## 13 4.8
## 14 4.3
## 15 5.8
## 16 5.7
## 17 5.4
## 18 5.1
## 19 5.7
## 20 5.1
## 21 5.4
## 22 5.1
## 23 4.6
## 24 5.1
## 25 4.8
## 26 5.0
## 27 5.0
## 28 5.2
## 29 5.2
## 30 4.7
## 31 4.8
## 32 5.4
## 33 5.2
## 34 5.5
## 35 4.9
## 36 5.0
## 37 5.5
## 38 4.9
## 39 4.4
## 40 5.1
## 41 5.0
## 42 4.5
## 43 4.4
## 44 5.0
## 45 5.1
## 46 4.8
## 47 5.1
## 48 4.6
## 49 5.3
## 50 5.0
## 51 7.0
## 52 6.4
## 53 6.9
## 54 5.5
## 55 6.5
## 56 5.7
## 57 6.3
## 58 4.9
## 59 6.6
## 60 5.2
## 61 5.0
## 62 5.9
## 63 6.0
## 64 6.1
## 65 5.6
## 66 6.7
## 67 5.6
## 68 5.8
## 69 6.2
## 70 5.6
## 71 5.9
## 72 6.1
## 73 6.3
## 74 6.1
## 75 6.4
## 76 6.6
## 77 6.8
## 78 6.7
## 79 6.0
## 80 5.7
## 81 5.5
## 82 5.5
## 83 5.8
## 84 6.0
## 85 5.4
## 86 6.0
## 87 6.7
## 88 6.3
## 89 5.6
## 90 5.5
## 91 5.5
## 92 6.1
## 93 5.8
## 94 5.0
## 95 5.6
## 96 5.7
## 97 5.7
## 98 6.2
## 99 5.1
## 100 5.7
## 101 6.3
## 102 5.8
## 103 7.1
## 104 6.3
## 105 6.5
## 106 7.6
## 107 4.9
## 108 7.3
## 109 6.7
## 110 7.2
## 111 6.5
## 112 6.4
## 113 6.8
## 114 5.7
## 115 5.8
## 116 6.4
## 117 6.5
## 118 7.7
## 119 7.7
## 120 6.0
## 121 6.9
## 122 5.6
## 123 7.7
## 124 6.3
## 125 6.7
## 126 7.2
## 127 6.2
## 128 6.1
## 129 6.4
## 130 7.2
## 131 7.4
## 132 7.9
## 133 6.4
## 134 6.3
## 135 6.1
## 136 7.7
## 137 6.3
## 138 6.4
## 139 6.0
## 140 6.9
## 141 6.7
## 142 6.9
## 143 5.8
## 144 6.8
## 145 6.7
## 146 6.7
## 147 6.3
## 148 6.5
## 149 6.2
## 150 5.9iris[,1]## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4
## [18] 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5
## [35] 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0
## [52] 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8
## [69] 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4
## [86] 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8
## [103] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7
## [120] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7
## [137] 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9as_tibble(iris)[1] ## # A tibble: 150 x 1
## Sepal.Length
## <dbl>
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4
## 7 4.6
## 8 5.0
## 9 4.4
## 10 4.9
## # ... with 140 more rowsas_tibble(iris)[,1]## # A tibble: 150 x 1
## Sepal.Length
## <dbl>
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4
## 7 4.6
## 8 5.0
## 9 4.4
## 10 4.9
## # ... with 140 more rowsas_tibble(iris)[[1]]## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4
## [18] 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5
## [35] 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0
## [52] 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8
## [69] 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4
## [86] 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8
## [103] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7
## [120] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7
## [137] 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9install.packages("nycflights13")
library(nycflights13)
library(nycflights13)
flights # On-time data for all flights that departed NYC (i.e. JFK, LGA or EWR) in 2013## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>summary(flights)## year month day dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907
## Median :2013 Median : 7.000 Median :16.00 Median :1401
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400
## NA's :8255
## sched_dep_time dep_delay arr_time sched_arr_time
## Min. : 106 Min. : -43.00 Min. : 1 Min. : 1
## 1st Qu.: 906 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124
## Median :1359 Median : -2.00 Median :1535 Median :1556
## Mean :1344 Mean : 12.64 Mean :1502 Mean :1536
## 3rd Qu.:1729 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945
## Max. :2359 Max. :1301.00 Max. :2400 Max. :2359
## NA's :8255 NA's :8713
## arr_delay carrier flight tailnum
## Min. : -86.000 Length:336776 Min. : 1 Length:336776
## 1st Qu.: -17.000 Class :character 1st Qu.: 553 Class :character
## Median : -5.000 Mode :character Median :1496 Mode :character
## Mean : 6.895 Mean :1972
## 3rd Qu.: 14.000 3rd Qu.:3465
## Max. :1272.000 Max. :8500
## NA's :9430
## origin dest air_time distance
## Length:336776 Length:336776 Min. : 20.0 Min. : 17
## Class :character Class :character 1st Qu.: 82.0 1st Qu.: 502
## Mode :character Mode :character Median :129.0 Median : 872
## Mean :150.7 Mean :1040
## 3rd Qu.:192.0 3rd Qu.:1389
## Max. :695.0 Max. :4983
## NA's :9430
## hour minute time_hour
## Min. : 1.00 Min. : 0.00 Min. :2013-01-01 05:00:00
## 1st Qu.: 9.00 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00
## Median :13.00 Median :29.00 Median :2013-07-03 10:00:00
## Mean :13.18 Mean :26.23 Mean :2013-07-03 05:02:36
## 3rd Qu.:17.00 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00
## Max. :23.00 Max. :59.00 Max. :2013-12-31 23:00:00
## Data Types
intintegersdbldoubles or real numberschrcharacter vectors (strings)dttmdate-timedatedatelgllogical (TRUE or FALSE)fctrfactors (catgeorical variables with fixed possible values, e.g., dropdown list)
flights[c("dep_time","tailnum","air_time","time_hour")]## # A tibble: 336,776 x 4
## dep_time tailnum air_time time_hour
## <int> <chr> <dbl> <dttm>
## 1 517 N14228 227 2013-01-01 05:00:00
## 2 533 N24211 227 2013-01-01 05:00:00
## 3 542 N619AA 160 2013-01-01 05:00:00
## 4 544 N804JB 183 2013-01-01 05:00:00
## 5 554 N668DN 116 2013-01-01 06:00:00
## 6 554 N39463 150 2013-01-01 05:00:00
## 7 555 N516JB 158 2013-01-01 06:00:00
## 8 557 N829AS 53 2013-01-01 06:00:00
## 9 557 N593JB 140 2013-01-01 06:00:00
## 10 558 N3ALAA 138 2013-01-01 06:00:00
## # ... with 336,766 more rowsdplyr and the 5 + 1 verbs of data manipulation
- dplyr is one of the packages in tidyverse which provides a consistent set of data manipulation verbs.
- filter
- Select based on values
- arrange
- reorder
- select & rename
- select based on names
- mutate & transmute
- add new variables that are functions of existing variables
- summarise
- condense multiple values to a single value
- group by
- perform any operation by group
filter
